Statistical heterogeneity increases our uncertainty about the magnitude, expected clinical variation, and clinical informativeness of the available evidence for the relative benefits and harms of a medical intervention. Random-effects models estimate the additional uncertainty that we have about the expected clinical variation in the relative effectiveness of a treatment. Quantitatively, the added uncertainty increases the variance and produces a wider CI. Dr. Lander and Ms. Keum and colleagues focus on a less discussed issue in meta-analysis: differences in the point estimate for the pooled treatment effect between the fixed- and random-effects models.

Random-effects models reweight the individual study estimates so that the results from smaller studies have a greater influence on the overall estimate. In general, the various random-effects models produce fairly similar estimates for the overall odds ratio that are closer to the estimates from the smaller, less precise studies. As Ms. Keum and colleagues note, random-effects models are susceptible to small-study effects. Thus, the less precise and perhaps lower-quality trials unduly influence the pooled treatment effect.